Overview

Dataset statistics

Number of variables81
Number of observations13734
Missing cells0
Missing cells (%)0.0%
Duplicate rows93
Duplicate rows (%)0.7%
Total size in memory8.5 MiB
Average record size in memory648.0 B

Variable types

Numeric32
Categorical49

Warnings

5 has constant value "1.0" Constant
6 has constant value "1.0" Constant
72 has constant value "1.0" Constant
73 has constant value "1.0" Constant
Dataset has 93 (0.7%) duplicate rows Duplicates
34 is highly correlated with 73 and 3 other fieldsHigh correlation
73 is highly correlated with 34 and 47 other fieldsHigh correlation
11 is highly correlated with 73 and 3 other fieldsHigh correlation
35 is highly correlated with 73 and 3 other fieldsHigh correlation
28 is highly correlated with 73 and 3 other fieldsHigh correlation
76 is highly correlated with 73 and 3 other fieldsHigh correlation
5 is highly correlated with 34 and 47 other fieldsHigh correlation
7 is highly correlated with 73 and 3 other fieldsHigh correlation
77 is highly correlated with 73 and 3 other fieldsHigh correlation
9 is highly correlated with 73 and 3 other fieldsHigh correlation
80 is highly correlated with 73 and 3 other fieldsHigh correlation
65 is highly correlated with 73 and 3 other fieldsHigh correlation
25 is highly correlated with 73 and 3 other fieldsHigh correlation
16 is highly correlated with 73 and 3 other fieldsHigh correlation
60 is highly correlated with 73 and 3 other fieldsHigh correlation
43 is highly correlated with 73 and 3 other fieldsHigh correlation
66 is highly correlated with 73 and 3 other fieldsHigh correlation
18 is highly correlated with 73 and 3 other fieldsHigh correlation
32 is highly correlated with 73 and 3 other fieldsHigh correlation
20 is highly correlated with 73 and 3 other fieldsHigh correlation
74 is highly correlated with 73 and 3 other fieldsHigh correlation
79 is highly correlated with 73 and 3 other fieldsHigh correlation
40 is highly correlated with 73 and 3 other fieldsHigh correlation
39 is highly correlated with 73 and 3 other fieldsHigh correlation
44 is highly correlated with 73 and 3 other fieldsHigh correlation
23 is highly correlated with 73 and 3 other fieldsHigh correlation
37 is highly correlated with 73 and 3 other fieldsHigh correlation
33 is highly correlated with 73 and 3 other fieldsHigh correlation
26 is highly correlated with 73 and 3 other fieldsHigh correlation
78 is highly correlated with 73 and 3 other fieldsHigh correlation
62 is highly correlated with 73 and 3 other fieldsHigh correlation
75 is highly correlated with 73 and 3 other fieldsHigh correlation
21 is highly correlated with 73 and 3 other fieldsHigh correlation
36 is highly correlated with 73 and 3 other fieldsHigh correlation
41 is highly correlated with 73 and 3 other fieldsHigh correlation
30 is highly correlated with 73 and 3 other fieldsHigh correlation
14 is highly correlated with 73 and 3 other fieldsHigh correlation
45 is highly correlated with 73 and 3 other fieldsHigh correlation
27 is highly correlated with 73 and 3 other fieldsHigh correlation
12 is highly correlated with 73 and 3 other fieldsHigh correlation
72 is highly correlated with 34 and 47 other fieldsHigh correlation
6 is highly correlated with 34 and 47 other fieldsHigh correlation
29 is highly correlated with 73 and 3 other fieldsHigh correlation
19 is highly correlated with 73 and 3 other fieldsHigh correlation
61 is highly correlated with 73 and 3 other fieldsHigh correlation
15 is highly correlated with 73 and 3 other fieldsHigh correlation
13 is highly correlated with 73 and 3 other fieldsHigh correlation
63 is highly correlated with 73 and 3 other fieldsHigh correlation
64 is highly correlated with 73 and 3 other fieldsHigh correlation
0 has 1471 (10.7%) zeros Zeros
8 has 459 (3.3%) zeros Zeros
10 has 162 (1.2%) zeros Zeros
17 has 144 (1.0%) zeros Zeros
22 has 449 (3.3%) zeros Zeros
31 has 142 (1.0%) zeros Zeros
48 has 2486 (18.1%) zeros Zeros
49 has 2587 (18.8%) zeros Zeros
50 has 5744 (41.8%) zeros Zeros
51 has 3687 (26.8%) zeros Zeros
52 has 6273 (45.7%) zeros Zeros
53 has 6988 (50.9%) zeros Zeros
54 has 6151 (44.8%) zeros Zeros
59 has 510 (3.7%) zeros Zeros
67 has 12658 (92.2%) zeros Zeros
68 has 11303 (82.3%) zeros Zeros
69 has 13591 (99.0%) zeros Zeros
71 has 9281 (67.6%) zeros Zeros

Reproduction

Analysis started2021-04-07 20:32:48.627342
Analysis finished2021-04-07 20:36:34.998428
Duration3 minutes and 46.37 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

0
Real number (ℝ≥0)

ZEROS

Distinct48
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.436762779
Minimum0
Maximum28
Zeros1471
Zeros (%)10.7%
Memory size107.4 KiB
2021-04-07T21:36:35.103708image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile14
Maximum28
Range28
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.42432201
Coefficient of variation (CV)0.8137787484
Kurtosis1.222840571
Mean5.436762779
Median Absolute Deviation (MAD)3
Skewness1.054651696
Sum74668.5
Variance19.57462524
MonotocityNot monotonic
2021-04-07T21:36:35.291751image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
01471
10.7%
31220
 
8.9%
11211
 
8.8%
21120
 
8.2%
41116
 
8.1%
51016
 
7.4%
61001
 
7.3%
7852
 
6.2%
8711
 
5.2%
9532
 
3.9%
Other values (38)3484
25.4%
ValueCountFrequency (%)
01471
10.7%
0.526
 
0.2%
11211
8.8%
1.5243
 
1.8%
21120
8.2%
ValueCountFrequency (%)
286
< 0.1%
275
< 0.1%
266
< 0.1%
246
< 0.1%
2310
0.1%

1
Real number (ℝ≥0)

Distinct39
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.95580312
Minimum15
Maximum44
Zeros0
Zeros (%)0.0%
Memory size107.4 KiB
2021-04-07T21:36:35.479934image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile20
Q125
median28
Q331
95-th percentile36
Maximum44
Range29
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.830556943
Coefficient of variation (CV)0.1727926371
Kurtosis-0.1163012392
Mean27.95580312
Median Absolute Deviation (MAD)3
Skewness0.05944891246
Sum383945
Variance23.33428038
MonotocityNot monotonic
2021-04-07T21:36:35.655167image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
301156
 
8.4%
291149
 
8.4%
281132
 
8.2%
271062
 
7.7%
261003
 
7.3%
31950
 
6.9%
25936
 
6.8%
32750
 
5.5%
24742
 
5.4%
23712
 
5.2%
Other values (29)4142
30.2%
ValueCountFrequency (%)
156
 
< 0.1%
1654
 
0.4%
17117
0.9%
17.51
 
< 0.1%
18161
1.2%
ValueCountFrequency (%)
4413
 
0.1%
4318
 
0.1%
4228
 
0.2%
4067
0.5%
3979
0.6%

2
Real number (ℝ≥0)

Distinct5876
Distinct (%)42.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.94152382
Minimum32.3
Maximum126.0294991
Zeros0
Zeros (%)0.0%
Memory size107.4 KiB
2021-04-07T21:36:35.856489image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum32.3
5-th percentile49.8449274
Q158.41534402
median62.49397283
Q367.00435391
95-th percentile75.52637116
Maximum126.0294991
Range93.7294991
Interquartile range (IQR)8.589009892

Descriptive statistics

Standard deviation8.628776486
Coefficient of variation (CV)0.137091954
Kurtosis4.889382659
Mean62.94152382
Median Absolute Deviation (MAD)4.267097206
Skewness1.032429177
Sum864438.8881
Variance74.45578364
MonotocityNot monotonic
2021-04-07T21:36:36.049525image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58.774904591231
 
9.0%
65.30642543956
 
7.0%
71.33335935784
 
5.7%
62.07853503516
 
3.8%
65.46628739461
 
3.4%
67.46650768252
 
1.8%
62.49397283252
 
1.8%
66.01927614251
 
1.8%
57.56881827239
 
1.7%
54.06506483216
 
1.6%
Other values (5866)8576
62.4%
ValueCountFrequency (%)
32.32
< 0.1%
33.565335521
< 0.1%
34.07487771
< 0.1%
34.592836211
< 0.1%
34.59821061
< 0.1%
ValueCountFrequency (%)
126.02949912
< 0.1%
124.06305971
< 0.1%
120.73228871
< 0.1%
120.49870931
< 0.1%
120.32172091
< 0.1%

3
Real number (ℝ≥0)

Distinct5881
Distinct (%)42.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean170.1272235
Minimum139.5121689
Maximum201.5196218
Zeros0
Zeros (%)0.0%
Memory size107.4 KiB
2021-04-07T21:36:36.245936image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum139.5121689
5-th percentile157.6208315
Q1166.660183
median170.5831143
Q3174.5971908
95-th percentile180.1367111
Maximum201.5196218
Range62.00745288
Interquartile range (IQR)7.937007854

Descriptive statistics

Standard deviation6.518813643
Coefficient of variation (CV)0.03831728696
Kurtosis1.131093874
Mean170.1272235
Median Absolute Deviation (MAD)3.972375866
Skewness-0.1424182904
Sum2336527.288
Variance42.49493131
MonotocityNot monotonic
2021-04-07T21:36:36.447885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
174.93547781231
 
9.0%
170.5831143956
 
7.0%
169.3890561784
 
5.7%
172.9434584516
 
3.8%
173.7438036461
 
3.4%
175.1077145252
 
1.8%
168.8989897251
 
1.8%
164.6890069250
 
1.8%
156.5070624239
 
1.7%
168.8406457216
 
1.6%
Other values (5871)8578
62.5%
ValueCountFrequency (%)
139.51216891
< 0.1%
145.95927611
< 0.1%
146.46712511
< 0.1%
146.8380041
< 0.1%
146.88052471
< 0.1%
ValueCountFrequency (%)
201.51962181
< 0.1%
200.72402051
< 0.1%
199.15405221
< 0.1%
199.05746951
< 0.1%
199.03111941
< 0.1%

4
Real number (ℝ≥0)

Distinct157
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.54536188
Minimum45
Maximum152
Zeros0
Zeros (%)0.0%
Memory size107.4 KiB
2021-04-07T21:36:36.666641image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum45
5-th percentile77
Q188.5
median94.5
Q3108
95-th percentile127
Maximum152
Range107
Interquartile range (IQR)19.5

Descriptive statistics

Standard deviation15.14738327
Coefficient of variation (CV)0.1537097534
Kurtosis0.09012948207
Mean98.54536188
Median Absolute Deviation (MAD)10
Skewness0.4538125163
Sum1353422
Variance229.4432199
MonotocityNot monotonic
2021-04-07T21:36:36.872795image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94.52356
 
17.2%
811306
 
9.5%
100581
 
4.2%
116371
 
2.7%
108326
 
2.4%
90247
 
1.8%
85246
 
1.8%
101240
 
1.7%
105233
 
1.7%
103225
 
1.6%
Other values (147)7603
55.4%
ValueCountFrequency (%)
451
 
< 0.1%
484
< 0.1%
493
< 0.1%
505
< 0.1%
512
 
< 0.1%
ValueCountFrequency (%)
1521
 
< 0.1%
1503
< 0.1%
1492
 
< 0.1%
1481
 
< 0.1%
1476
< 0.1%

5
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
1.0
13734 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.013734
100.0%
2021-04-07T21:36:37.205074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:36:37.305833image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.013734
100.0%

Most occurring characters

ValueCountFrequency (%)
113734
33.3%
.13734
33.3%
013734
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
113734
50.0%
013734
50.0%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
113734
33.3%
.13734
33.3%
013734
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
113734
33.3%
.13734
33.3%
013734
33.3%

6
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
1.0
13734 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.013734
100.0%
2021-04-07T21:36:37.552780image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:36:37.653468image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.013734
100.0%

Most occurring characters

ValueCountFrequency (%)
113734
33.3%
.13734
33.3%
013734
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
113734
50.0%
013734
50.0%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
113734
33.3%
.13734
33.3%
013734
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
113734
33.3%
.13734
33.3%
013734
33.3%

7
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
1.0
8482 
0.5
3639 
0.0
1613 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.5
4th row0.5
5th row0.0
ValueCountFrequency (%)
1.08482
61.8%
0.53639
26.5%
0.01613
 
11.7%
2021-04-07T21:36:37.927845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:36:38.032225image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.08482
61.8%
0.53639
26.5%
0.01613
 
11.7%

Most occurring characters

ValueCountFrequency (%)
015347
37.2%
.13734
33.3%
18482
20.6%
53639
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
015347
55.9%
18482
30.9%
53639
 
13.2%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
015347
37.2%
.13734
33.3%
18482
20.6%
53639
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
015347
37.2%
.13734
33.3%
18482
20.6%
53639
 
8.8%

8
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.510157274
Minimum0
Maximum3
Zeros459
Zeros (%)3.3%
Memory size107.4 KiB
2021-04-07T21:36:38.135518image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5
Q11
median1.5
Q32
95-th percentile3
Maximum3
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7951938689
Coefficient of variation (CV)0.5265636121
Kurtosis-0.5961385637
Mean1.510157274
Median Absolute Deviation (MAD)0.5
Skewness0.3424662537
Sum20740.5
Variance0.6323332892
MonotocityNot monotonic
2021-04-07T21:36:38.269233image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
13601
26.2%
1.52924
21.3%
22662
19.4%
0.51795
13.1%
31599
11.6%
2.5694
 
5.1%
0459
 
3.3%
ValueCountFrequency (%)
0459
 
3.3%
0.51795
13.1%
13601
26.2%
1.52924
21.3%
22662
19.4%
ValueCountFrequency (%)
31599
11.6%
2.5694
 
5.1%
22662
19.4%
1.52924
21.3%
13601
26.2%

9
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
1.0
5771 
0.5
2537 
1.5
2410 
2.0
1968 
0.0
1048 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.5
3rd row0.5
4th row0.5
5th row0.0
ValueCountFrequency (%)
1.05771
42.0%
0.52537
18.5%
1.52410
17.5%
2.01968
 
14.3%
0.01048
 
7.6%
2021-04-07T21:36:38.602826image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:36:38.710346image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.05771
42.0%
0.52537
18.5%
1.52410
17.5%
2.01968
 
14.3%
0.01048
 
7.6%

Most occurring characters

ValueCountFrequency (%)
.13734
33.3%
012372
30.0%
18181
19.9%
54947
 
12.0%
21968
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
012372
45.0%
18181
29.8%
54947
 
18.0%
21968
 
7.2%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
.13734
33.3%
012372
30.0%
18181
19.9%
54947
 
12.0%
21968
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
.13734
33.3%
012372
30.0%
18181
19.9%
54947
 
12.0%
21968
 
4.8%

10
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.165319645
Minimum0
Maximum3
Zeros162
Zeros (%)1.2%
Memory size107.4 KiB
2021-04-07T21:36:38.838484image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2.5
Q32.5
95-th percentile3
Maximum3
Range3
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.7199597747
Coefficient of variation (CV)0.3324958403
Kurtosis-0.03623347958
Mean2.165319645
Median Absolute Deviation (MAD)0.5
Skewness-0.8138053598
Sum29738.5
Variance0.5183420772
MonotocityNot monotonic
2021-04-07T21:36:38.973349image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2.54207
30.6%
23428
25.0%
33117
22.7%
11840
13.4%
1.5684
 
5.0%
0.5296
 
2.2%
0162
 
1.2%
ValueCountFrequency (%)
0162
 
1.2%
0.5296
 
2.2%
11840
13.4%
1.5684
 
5.0%
23428
25.0%
ValueCountFrequency (%)
33117
22.7%
2.54207
30.6%
23428
25.0%
1.5684
 
5.0%
11840
13.4%

11
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
9681 
0.5
2506 
1.0
1547 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.5
3rd row0.5
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09681
70.5%
0.52506
 
18.2%
1.01547
 
11.3%
2021-04-07T21:36:39.329476image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:36:39.433055image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.09681
70.5%
0.52506
 
18.2%
1.01547
 
11.3%

Most occurring characters

ValueCountFrequency (%)
023415
56.8%
.13734
33.3%
52506
 
6.1%
11547
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
023415
85.2%
52506
 
9.1%
11547
 
5.6%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
023415
56.8%
.13734
33.3%
52506
 
6.1%
11547
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
023415
56.8%
.13734
33.3%
52506
 
6.1%
11547
 
3.8%

12
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
1.0
5627 
0.0
3191 
2.0
1806 
1.5
1622 
0.5
1488 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row0.5
5th row2.0
ValueCountFrequency (%)
1.05627
41.0%
0.03191
23.2%
2.01806
 
13.1%
1.51622
 
11.8%
0.51488
 
10.8%
2021-04-07T21:36:39.699898image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:36:39.808453image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.05627
41.0%
0.03191
23.2%
2.01806
 
13.1%
1.51622
 
11.8%
0.51488
 
10.8%

Most occurring characters

ValueCountFrequency (%)
015303
37.1%
.13734
33.3%
17249
17.6%
53110
 
7.5%
21806
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
015303
55.7%
17249
26.4%
53110
 
11.3%
21806
 
6.6%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
015303
37.1%
.13734
33.3%
17249
17.6%
53110
 
7.5%
21806
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
015303
37.1%
.13734
33.3%
17249
17.6%
53110
 
7.5%
21806
 
4.4%

13
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
1.5
3800 
2.0
3645 
1.0
2521 
2.5
2100 
3.0
1668 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row2.5
4th row2.5
5th row2.0
ValueCountFrequency (%)
1.53800
27.7%
2.03645
26.5%
1.02521
18.4%
2.52100
15.3%
3.01668
12.1%
2021-04-07T21:36:40.114894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:36:40.221707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.53800
27.7%
2.03645
26.5%
1.02521
18.4%
2.52100
15.3%
3.01668
12.1%

Most occurring characters

ValueCountFrequency (%)
.13734
33.3%
07834
19.0%
16321
15.3%
55900
14.3%
25745
13.9%
31668
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
07834
28.5%
16321
23.0%
55900
21.5%
25745
20.9%
31668
 
6.1%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
.13734
33.3%
07834
19.0%
16321
15.3%
55900
14.3%
25745
13.9%
31668
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
.13734
33.3%
07834
19.0%
16321
15.3%
55900
14.3%
25745
13.9%
31668
 
4.0%

14
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
2.0
4157 
2.5
3259 
3.0
3212 
1.0
1640 
1.5
1466 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.5
3rd row3.0
4th row1.5
5th row3.0
ValueCountFrequency (%)
2.04157
30.3%
2.53259
23.7%
3.03212
23.4%
1.01640
 
11.9%
1.51466
 
10.7%
2021-04-07T21:36:40.539514image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:36:40.646789image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
2.04157
30.3%
2.53259
23.7%
3.03212
23.4%
1.01640
 
11.9%
1.51466
 
10.7%

Most occurring characters

ValueCountFrequency (%)
.13734
33.3%
09009
21.9%
27416
18.0%
54725
 
11.5%
33212
 
7.8%
13106
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
09009
32.8%
27416
27.0%
54725
17.2%
33212
 
11.7%
13106
 
11.3%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
.13734
33.3%
09009
21.9%
27416
18.0%
54725
 
11.5%
33212
 
7.8%
13106
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
.13734
33.3%
09009
21.9%
27416
18.0%
54725
 
11.5%
33212
 
7.8%
13106
 
7.5%

15
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
1.0
4287 
0.0
3645 
1.5
3330 
0.5
1526 
2.0
946 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.5
3rd row0.0
4th row1.0
5th row0.0
ValueCountFrequency (%)
1.04287
31.2%
0.03645
26.5%
1.53330
24.2%
0.51526
 
11.1%
2.0946
 
6.9%
2021-04-07T21:36:40.970224image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:36:41.079389image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.04287
31.2%
0.03645
26.5%
1.53330
24.2%
0.51526
 
11.1%
2.0946
 
6.9%

Most occurring characters

ValueCountFrequency (%)
014049
34.1%
.13734
33.3%
17617
18.5%
54856
 
11.8%
2946
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
014049
51.1%
17617
27.7%
54856
 
17.7%
2946
 
3.4%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
014049
34.1%
.13734
33.3%
17617
18.5%
54856
 
11.8%
2946
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
014049
34.1%
.13734
33.3%
17617
18.5%
54856
 
11.8%
2946
 
2.3%

16
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
1.0
7836 
0.5
3841 
0.0
2057 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
1.07836
57.1%
0.53841
28.0%
0.02057
 
15.0%
2021-04-07T21:36:41.399583image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:36:41.503966image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.07836
57.1%
0.53841
28.0%
0.02057
 
15.0%

Most occurring characters

ValueCountFrequency (%)
015791
38.3%
.13734
33.3%
17836
19.0%
53841
 
9.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
015791
57.5%
17836
28.5%
53841
 
14.0%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
015791
38.3%
.13734
33.3%
17836
19.0%
53841
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
015791
38.3%
.13734
33.3%
17836
19.0%
53841
 
9.3%

17
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.041065968
Minimum0
Maximum3
Zeros144
Zeros (%)1.0%
Memory size107.4 KiB
2021-04-07T21:36:41.608599image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q32.5
95-th percentile3
Maximum3
Range3
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.6070856879
Coefficient of variation (CV)0.2974356035
Kurtosis0.2899920713
Mean2.041065968
Median Absolute Deviation (MAD)0.5
Skewness-0.4715638811
Sum28032
Variance0.3685530324
MonotocityNot monotonic
2021-04-07T21:36:41.736146image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
25597
40.8%
2.53017
22.0%
31752
 
12.8%
1.51634
 
11.9%
11587
 
11.6%
0144
 
1.0%
0.53
 
< 0.1%
ValueCountFrequency (%)
0144
 
1.0%
0.53
 
< 0.1%
11587
 
11.6%
1.51634
 
11.9%
25597
40.8%
ValueCountFrequency (%)
31752
 
12.8%
2.53017
22.0%
25597
40.8%
1.51634
 
11.9%
11587
 
11.6%

18
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
2.0
6338 
1.0
2931 
1.5
2278 
3.0
1682 
2.5
 
505

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row2.0
4th row1.5
5th row1.0
ValueCountFrequency (%)
2.06338
46.1%
1.02931
21.3%
1.52278
 
16.6%
3.01682
 
12.2%
2.5505
 
3.7%
2021-04-07T21:36:42.066346image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:36:42.173980image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
2.06338
46.1%
1.02931
21.3%
1.52278
 
16.6%
3.01682
 
12.2%
2.5505
 
3.7%

Most occurring characters

ValueCountFrequency (%)
.13734
33.3%
010951
26.6%
26843
16.6%
15209
 
12.6%
52783
 
6.8%
31682
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
010951
39.9%
26843
24.9%
15209
19.0%
52783
 
10.1%
31682
 
6.1%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
.13734
33.3%
010951
26.6%
26843
16.6%
15209
 
12.6%
52783
 
6.8%
31682
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
.13734
33.3%
010951
26.6%
26843
16.6%
15209
 
12.6%
52783
 
6.8%
31682
 
4.1%

19
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
1.0
10973 
0.5
1738 
0.0
 
1023

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0
ValueCountFrequency (%)
1.010973
79.9%
0.51738
 
12.7%
0.01023
 
7.4%
2021-04-07T21:36:42.478587image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:36:42.582213image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.010973
79.9%
0.51738
 
12.7%
0.01023
 
7.4%

Most occurring characters

ValueCountFrequency (%)
014757
35.8%
.13734
33.3%
110973
26.6%
51738
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
014757
53.7%
110973
39.9%
51738
 
6.3%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
014757
35.8%
.13734
33.3%
110973
26.6%
51738
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
014757
35.8%
.13734
33.3%
110973
26.6%
51738
 
4.2%

20
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
1.5
5903 
1.0
5587 
2.0
2244 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.5
4th row1.5
5th row1.0
ValueCountFrequency (%)
1.55903
43.0%
1.05587
40.7%
2.02244
 
16.3%
2021-04-07T21:36:42.885621image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:36:42.989520image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.55903
43.0%
1.05587
40.7%
2.02244
 
16.3%

Most occurring characters

ValueCountFrequency (%)
.13734
33.3%
111490
27.9%
07831
19.0%
55903
14.3%
22244
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
111490
41.8%
07831
28.5%
55903
21.5%
22244
 
8.2%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
.13734
33.3%
111490
27.9%
07831
19.0%
55903
14.3%
22244
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
.13734
33.3%
111490
27.9%
07831
19.0%
55903
14.3%
22244
 
5.4%

21
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
1.0
8735 
0.5
3454 
0.0
1545 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.5
4th row0.5
5th row0.0
ValueCountFrequency (%)
1.08735
63.6%
0.53454
 
25.1%
0.01545
 
11.2%
2021-04-07T21:36:43.295364image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:36:43.400232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.08735
63.6%
0.53454
 
25.1%
0.01545
 
11.2%

Most occurring characters

ValueCountFrequency (%)
015279
37.1%
.13734
33.3%
18735
21.2%
53454
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
015279
55.6%
18735
31.8%
53454
 
12.6%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
015279
37.1%
.13734
33.3%
18735
21.2%
53454
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
015279
37.1%
.13734
33.3%
18735
21.2%
53454
 
8.4%

22
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.607543323
Minimum0
Maximum3
Zeros449
Zeros (%)3.3%
Memory size107.4 KiB
2021-04-07T21:36:43.504542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1.5
Q32
95-th percentile3
Maximum3
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7106075702
Coefficient of variation (CV)0.4420456731
Kurtosis-0.3807193813
Mean1.607543323
Median Absolute Deviation (MAD)0.5
Skewness0.2134802026
Sum22078
Variance0.5049631189
MonotocityNot monotonic
2021-04-07T21:36:43.633996image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
14846
35.3%
23673
26.7%
1.52300
16.7%
31190
 
8.7%
2.51114
 
8.1%
0449
 
3.3%
0.5162
 
1.2%
ValueCountFrequency (%)
0449
 
3.3%
0.5162
 
1.2%
14846
35.3%
1.52300
16.7%
23673
26.7%
ValueCountFrequency (%)
31190
 
8.7%
2.51114
 
8.1%
23673
26.7%
1.52300
16.7%
14846
35.3%

23
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
1.0
5955 
0.5
2522 
1.5
2347 
2.0
1758 
0.0
1152 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.5
4th row0.5
5th row0.0
ValueCountFrequency (%)
1.05955
43.4%
0.52522
18.4%
1.52347
 
17.1%
2.01758
 
12.8%
0.01152
 
8.4%
2021-04-07T21:36:43.963439image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:36:44.988050image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.05955
43.4%
0.52522
18.4%
1.52347
 
17.1%
2.01758
 
12.8%
0.01152
 
8.4%

Most occurring characters

ValueCountFrequency (%)
.13734
33.3%
012539
30.4%
18302
20.1%
54869
 
11.8%
21758
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
012539
45.6%
18302
30.2%
54869
 
17.7%
21758
 
6.4%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
.13734
33.3%
012539
30.4%
18302
20.1%
54869
 
11.8%
21758
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
.13734
33.3%
012539
30.4%
18302
20.1%
54869
 
11.8%
21758
 
4.3%

24
Real number (ℝ≥0)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.102009611
Minimum0
Maximum3
Zeros96
Zeros (%)0.7%
Memory size107.4 KiB
2021-04-07T21:36:45.114543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q32.5
95-th percentile3
Maximum3
Range3
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.5997392899
Coefficient of variation (CV)0.2853171016
Kurtosis0.1386363217
Mean2.102009611
Median Absolute Deviation (MAD)0.5
Skewness-0.5600831847
Sum28869
Variance0.3596872158
MonotocityNot monotonic
2021-04-07T21:36:45.245154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
24804
35.0%
2.54029
29.3%
31820
 
13.3%
11492
 
10.9%
1.51490
 
10.8%
096
 
0.7%
0.53
 
< 0.1%
ValueCountFrequency (%)
096
 
0.7%
0.53
 
< 0.1%
11492
 
10.9%
1.51490
 
10.8%
24804
35.0%
ValueCountFrequency (%)
31820
 
13.3%
2.54029
29.3%
24804
35.0%
1.51490
 
10.8%
11492
 
10.9%

25
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
8244 
0.5
3924 
1.0
1566 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.5
3rd row0.5
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.08244
60.0%
0.53924
28.6%
1.01566
 
11.4%
2021-04-07T21:36:45.564717image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:36:45.669529image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.08244
60.0%
0.53924
28.6%
1.01566
 
11.4%

Most occurring characters

ValueCountFrequency (%)
021978
53.3%
.13734
33.3%
53924
 
9.5%
11566
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
021978
80.0%
53924
 
14.3%
11566
 
5.7%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
021978
53.3%
.13734
33.3%
53924
 
9.5%
11566
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
021978
53.3%
.13734
33.3%
53924
 
9.5%
11566
 
3.8%

26
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
1.0
6082 
0.0
2975 
0.5
2025 
2.0
1386 
1.5
1266 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.5
3rd row1.0
4th row0.5
5th row2.0
ValueCountFrequency (%)
1.06082
44.3%
0.02975
21.7%
0.52025
 
14.7%
2.01386
 
10.1%
1.51266
 
9.2%
2021-04-07T21:36:45.945086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:36:46.053146image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.06082
44.3%
0.02975
21.7%
0.52025
 
14.7%
2.01386
 
10.1%
1.51266
 
9.2%

Most occurring characters

ValueCountFrequency (%)
015443
37.5%
.13734
33.3%
17348
17.8%
53291
 
8.0%
21386
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
015443
56.2%
17348
26.8%
53291
 
12.0%
21386
 
5.0%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
015443
37.5%
.13734
33.3%
17348
17.8%
53291
 
8.0%
21386
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
015443
37.5%
.13734
33.3%
17348
17.8%
53291
 
8.0%
21386
 
3.4%

27
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
2.0
4516 
2.5
3153 
1.0
2906 
1.5
2156 
3.0
1003 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row2.5
4th row2.0
5th row2.0
ValueCountFrequency (%)
2.04516
32.9%
2.53153
23.0%
1.02906
21.2%
1.52156
15.7%
3.01003
 
7.3%
2021-04-07T21:36:46.369633image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:36:46.477269image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
2.04516
32.9%
2.53153
23.0%
1.02906
21.2%
1.52156
15.7%
3.01003
 
7.3%

Most occurring characters

ValueCountFrequency (%)
.13734
33.3%
08425
20.4%
27669
18.6%
55309
 
12.9%
15062
 
12.3%
31003
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
08425
30.7%
27669
27.9%
55309
19.3%
15062
18.4%
31003
 
3.7%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
.13734
33.3%
08425
20.4%
27669
18.6%
55309
 
12.9%
15062
 
12.3%
31003
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
.13734
33.3%
08425
20.4%
27669
18.6%
55309
 
12.9%
15062
 
12.3%
31003
 
2.4%

28
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
2.0
4789 
2.5
3460 
3.0
3162 
1.5
1190 
1.0
1133 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row3.0
4th row2.0
5th row2.0
ValueCountFrequency (%)
2.04789
34.9%
2.53460
25.2%
3.03162
23.0%
1.51190
 
8.7%
1.01133
 
8.2%
2021-04-07T21:36:46.809635image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:36:46.918232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
2.04789
34.9%
2.53460
25.2%
3.03162
23.0%
1.51190
 
8.7%
1.01133
 
8.2%

Most occurring characters

ValueCountFrequency (%)
.13734
33.3%
09084
22.0%
28249
20.0%
54650
 
11.3%
33162
 
7.7%
12323
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
09084
33.1%
28249
30.0%
54650
16.9%
33162
 
11.5%
12323
 
8.5%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
.13734
33.3%
09084
22.0%
28249
20.0%
54650
 
11.3%
33162
 
7.7%
12323
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
.13734
33.3%
09084
22.0%
28249
20.0%
54650
 
11.3%
33162
 
7.7%
12323
 
5.6%

29
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
1.5
3473 
1.0
3288 
0.0
3218 
2.0
2822 
0.5
933 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.5
3rd row0.0
4th row1.5
5th row0.0
ValueCountFrequency (%)
1.53473
25.3%
1.03288
23.9%
0.03218
23.4%
2.02822
20.5%
0.5933
 
6.8%
2021-04-07T21:36:47.226594image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:36:47.335003image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.53473
25.3%
1.03288
23.9%
0.03218
23.4%
2.02822
20.5%
0.5933
 
6.8%

Most occurring characters

ValueCountFrequency (%)
.13734
33.3%
013479
32.7%
16761
16.4%
54406
 
10.7%
22822
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
013479
49.1%
16761
24.6%
54406
 
16.0%
22822
 
10.3%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
.13734
33.3%
013479
32.7%
16761
16.4%
54406
 
10.7%
22822
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
.13734
33.3%
013479
32.7%
16761
16.4%
54406
 
10.7%
22822
 
6.8%

30
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
1.0
9824 
0.5
2828 
0.0
1082 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.09824
71.5%
0.52828
 
20.6%
0.01082
 
7.9%
2021-04-07T21:36:47.644374image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:36:47.749646image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.09824
71.5%
0.52828
 
20.6%
0.01082
 
7.9%

Most occurring characters

ValueCountFrequency (%)
014816
36.0%
.13734
33.3%
19824
23.8%
52828
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
014816
53.9%
19824
35.8%
52828
 
10.3%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
014816
36.0%
.13734
33.3%
19824
23.8%
52828
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
014816
36.0%
.13734
33.3%
19824
23.8%
52828
 
6.9%

31
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.083952235
Minimum0
Maximum3
Zeros142
Zeros (%)1.0%
Memory size107.4 KiB
2021-04-07T21:36:47.854452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q32.5
95-th percentile3
Maximum3
Range3
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.6761104915
Coefficient of variation (CV)0.3244366546
Kurtosis-0.2746602078
Mean2.083952235
Median Absolute Deviation (MAD)0.5
Skewness-0.4035785846
Sum28621
Variance0.4571253967
MonotocityNot monotonic
2021-04-07T21:36:47.984069image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
25423
39.5%
32951
21.5%
2.52126
 
15.5%
12044
 
14.9%
1.51039
 
7.6%
0142
 
1.0%
0.59
 
0.1%
ValueCountFrequency (%)
0142
 
1.0%
0.59
 
0.1%
12044
 
14.9%
1.51039
 
7.6%
25423
39.5%
ValueCountFrequency (%)
32951
21.5%
2.52126
 
15.5%
25423
39.5%
1.51039
 
7.6%
12044
 
14.9%

32
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
2.0
3800 
1.5
3748 
2.5
2780 
1.0
2235 
3.0
1171 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.5
3rd row1.5
4th row2.0
5th row1.0
ValueCountFrequency (%)
2.03800
27.7%
1.53748
27.3%
2.52780
20.2%
1.02235
16.3%
3.01171
 
8.5%
2021-04-07T21:36:48.308071image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:36:48.416565image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
2.03800
27.7%
1.53748
27.3%
2.52780
20.2%
1.02235
16.3%
3.01171
 
8.5%

Most occurring characters

ValueCountFrequency (%)
.13734
33.3%
07206
17.5%
26580
16.0%
56528
15.8%
15983
14.5%
31171
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
07206
26.2%
26580
24.0%
56528
23.8%
15983
21.8%
31171
 
4.3%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
.13734
33.3%
07206
17.5%
26580
16.0%
56528
15.8%
15983
14.5%
31171
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
.13734
33.3%
07206
17.5%
26580
16.0%
56528
15.8%
15983
14.5%
31171
 
2.8%

33
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
1.0
5941 
0.5
4021 
0.0
3772 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.5
4th row0.5
5th row0.0
ValueCountFrequency (%)
1.05941
43.3%
0.54021
29.3%
0.03772
27.5%
2021-04-07T21:36:48.723124image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:36:48.828302image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.05941
43.3%
0.54021
29.3%
0.03772
27.5%

Most occurring characters

ValueCountFrequency (%)
017506
42.5%
.13734
33.3%
15941
 
14.4%
54021
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
017506
63.7%
15941
 
21.6%
54021
 
14.6%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
017506
42.5%
.13734
33.3%
15941
 
14.4%
54021
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
017506
42.5%
.13734
33.3%
15941
 
14.4%
54021
 
9.8%

34
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
1.0
7317 
1.5
4134 
2.0
2283 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.5
4th row1.5
5th row1.0
ValueCountFrequency (%)
1.07317
53.3%
1.54134
30.1%
2.02283
 
16.6%
2021-04-07T21:36:49.097351image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:36:49.202401image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.07317
53.3%
1.54134
30.1%
2.02283
 
16.6%

Most occurring characters

ValueCountFrequency (%)
.13734
33.3%
111451
27.8%
09600
23.3%
54134
 
10.0%
22283
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
111451
41.7%
09600
34.9%
54134
 
15.1%
22283
 
8.3%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
.13734
33.3%
111451
27.8%
09600
23.3%
54134
 
10.0%
22283
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
.13734
33.3%
111451
27.8%
09600
23.3%
54134
 
10.0%
22283
 
5.5%

35
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
1.0
9100 
0.0
3302 
0.5
1332 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.5
5th row1.0
ValueCountFrequency (%)
1.09100
66.3%
0.03302
 
24.0%
0.51332
 
9.7%
2021-04-07T21:36:49.517130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:36:49.621783image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.09100
66.3%
0.03302
 
24.0%
0.51332
 
9.7%

Most occurring characters

ValueCountFrequency (%)
017036
41.3%
.13734
33.3%
19100
22.1%
51332
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
017036
62.0%
19100
33.1%
51332
 
4.8%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
017036
41.3%
.13734
33.3%
19100
22.1%
51332
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
017036
41.3%
.13734
33.3%
19100
22.1%
51332
 
3.2%

36
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
1.0
9821 
0.5
2421 
0.0
1492 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.5
4th row1.0
5th row0.5
ValueCountFrequency (%)
1.09821
71.5%
0.52421
 
17.6%
0.01492
 
10.9%
2021-04-07T21:36:49.904991image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:36:50.009499image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.09821
71.5%
0.52421
 
17.6%
0.01492
 
10.9%

Most occurring characters

ValueCountFrequency (%)
015226
37.0%
.13734
33.3%
19821
23.8%
52421
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
015226
55.4%
19821
35.8%
52421
 
8.8%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
015226
37.0%
.13734
33.3%
19821
23.8%
52421
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
015226
37.0%
.13734
33.3%
19821
23.8%
52421
 
5.9%

37
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
1.0
10799 
0.0
1600 
0.5
1335 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0
ValueCountFrequency (%)
1.010799
78.6%
0.01600
 
11.6%
0.51335
 
9.7%
2021-04-07T21:36:50.293430image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:36:50.398817image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.010799
78.6%
0.01600
 
11.6%
0.51335
 
9.7%

Most occurring characters

ValueCountFrequency (%)
015334
37.2%
.13734
33.3%
110799
26.2%
51335
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
015334
55.8%
110799
39.3%
51335
 
4.9%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
015334
37.2%
.13734
33.3%
110799
26.2%
51335
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
015334
37.2%
.13734
33.3%
110799
26.2%
51335
 
3.2%

38
Real number (ℝ≥0)

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.02075142
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Memory size107.4 KiB
2021-04-07T21:36:50.508564image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q13.5
median4
Q34.5
95-th percentile6
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9182362353
Coefficient of variation (CV)0.2283742862
Kurtosis2.589931606
Mean4.02075142
Median Absolute Deviation (MAD)0.5
Skewness0.7726407447
Sum55221
Variance0.8431577838
MonotocityNot monotonic
2021-04-07T21:36:50.659286image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
45956
43.4%
32049
 
14.9%
51998
 
14.5%
3.51604
 
11.7%
4.5701
 
5.1%
6599
 
4.4%
2392
 
2.9%
2.5186
 
1.4%
7139
 
1.0%
839
 
0.3%
Other values (3)71
 
0.5%
ValueCountFrequency (%)
120
 
0.1%
2392
 
2.9%
2.5186
 
1.4%
32049
14.9%
3.51604
11.7%
ValueCountFrequency (%)
918
 
0.1%
839
 
0.3%
7139
 
1.0%
6599
4.4%
5.533
 
0.2%

39
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
8566 
1.0
3518 
0.5
1650 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.5
3rd row1.0
4th row0.5
5th row0.0
ValueCountFrequency (%)
0.08566
62.4%
1.03518
25.6%
0.51650
 
12.0%
2021-04-07T21:36:51.011173image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:36:51.115733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.08566
62.4%
1.03518
25.6%
0.51650
 
12.0%

Most occurring characters

ValueCountFrequency (%)
022300
54.1%
.13734
33.3%
13518
 
8.5%
51650
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
022300
81.2%
13518
 
12.8%
51650
 
6.0%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
022300
54.1%
.13734
33.3%
13518
 
8.5%
51650
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
022300
54.1%
.13734
33.3%
13518
 
8.5%
51650
 
4.0%

40
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
10386 
1.0
2158 
0.5
1190 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.010386
75.6%
1.02158
 
15.7%
0.51190
 
8.7%
2021-04-07T21:36:51.398816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:36:51.504135image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.010386
75.6%
1.02158
 
15.7%
0.51190
 
8.7%

Most occurring characters

ValueCountFrequency (%)
024120
58.5%
.13734
33.3%
12158
 
5.2%
51190
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
024120
87.8%
12158
 
7.9%
51190
 
4.3%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
024120
58.5%
.13734
33.3%
12158
 
5.2%
51190
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
024120
58.5%
.13734
33.3%
12158
 
5.2%
51190
 
2.9%

41
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
11099 
1.0
1997 
0.5
 
638

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.011099
80.8%
1.01997
 
14.5%
0.5638
 
4.6%
2021-04-07T21:36:51.787590image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:36:51.892180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.011099
80.8%
1.01997
 
14.5%
0.5638
 
4.6%

Most occurring characters

ValueCountFrequency (%)
024833
60.3%
.13734
33.3%
11997
 
4.8%
5638
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
024833
90.4%
11997
 
7.3%
5638
 
2.3%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
024833
60.3%
.13734
33.3%
11997
 
4.8%
5638
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
024833
60.3%
.13734
33.3%
11997
 
4.8%
5638
 
1.5%

42
Real number (ℝ≥0)

Distinct23
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.935270133
Minimum3
Maximum15
Zeros0
Zeros (%)0.0%
Memory size107.4 KiB
2021-04-07T21:36:52.006962image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile7
Q19
median10
Q311
95-th percentile13
Maximum15
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.910992837
Coefficient of variation (CV)0.192344326
Kurtosis0.9118687496
Mean9.935270133
Median Absolute Deviation (MAD)1
Skewness-0.3256962849
Sum136451
Variance3.651893625
MonotocityNot monotonic
2021-04-07T21:36:52.182183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
93587
26.1%
102093
15.2%
121901
13.8%
111800
13.1%
8843
 
6.1%
10.5598
 
4.4%
13502
 
3.7%
7455
 
3.3%
6305
 
2.2%
9.5275
 
2.0%
Other values (13)1375
 
10.0%
ValueCountFrequency (%)
367
0.5%
479
0.6%
4.51
 
< 0.1%
5141
1.0%
5.513
 
0.1%
ValueCountFrequency (%)
15139
 
1.0%
14206
1.5%
13.534
 
0.2%
13502
3.7%
12.5106
 
0.8%

43
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
12453 
1.0
 
1021
0.5
 
260

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0
ValueCountFrequency (%)
0.012453
90.7%
1.01021
 
7.4%
0.5260
 
1.9%
2021-04-07T21:36:52.525565image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:36:52.630152image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.012453
90.7%
1.01021
 
7.4%
0.5260
 
1.9%

Most occurring characters

ValueCountFrequency (%)
026187
63.6%
.13734
33.3%
11021
 
2.5%
5260
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
026187
95.3%
11021
 
3.7%
5260
 
0.9%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
026187
63.6%
.13734
33.3%
11021
 
2.5%
5260
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
026187
63.6%
.13734
33.3%
11021
 
2.5%
5260
 
0.6%

44
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
13364 
1.0
 
293
0.5
 
77

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.013364
97.3%
1.0293
 
2.1%
0.577
 
0.6%
2021-04-07T21:36:52.935004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:36:53.040367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.013364
97.3%
1.0293
 
2.1%
0.577
 
0.6%

Most occurring characters

ValueCountFrequency (%)
027098
65.8%
.13734
33.3%
1293
 
0.7%
577
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
027098
98.7%
1293
 
1.1%
577
 
0.3%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
027098
65.8%
.13734
33.3%
1293
 
0.7%
577
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
027098
65.8%
.13734
33.3%
1293
 
0.7%
577
 
0.2%

45
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
10785 
1.0
2402 
0.5
 
547

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.010785
78.5%
1.02402
 
17.5%
0.5547
 
4.0%
2021-04-07T21:36:53.323129image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:36:53.437409image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.010785
78.5%
1.02402
 
17.5%
0.5547
 
4.0%

Most occurring characters

ValueCountFrequency (%)
024519
59.5%
.13734
33.3%
12402
 
5.8%
5547
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
024519
89.3%
12402
 
8.7%
5547
 
2.0%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
024519
59.5%
.13734
33.3%
12402
 
5.8%
5547
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
024519
59.5%
.13734
33.3%
12402
 
5.8%
5547
 
1.3%

46
Real number (ℝ≥0)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.906691423
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Memory size107.4 KiB
2021-04-07T21:36:53.548441image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33.5
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.195916955
Coefficient of variation (CV)0.4114358153
Kurtosis0.03486966308
Mean2.906691423
Median Absolute Deviation (MAD)1
Skewness0.6150996158
Sum39920.5
Variance1.430217364
MonotocityNot monotonic
2021-04-07T21:36:53.693272image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
34106
29.9%
24078
29.7%
41658
12.1%
11221
 
8.9%
51158
 
8.4%
2.5575
 
4.2%
6327
 
2.4%
3.5286
 
2.1%
4.5173
 
1.3%
1.563
 
0.5%
Other values (2)89
 
0.6%
ValueCountFrequency (%)
11221
 
8.9%
1.563
 
0.5%
24078
29.7%
2.5575
 
4.2%
34106
29.9%
ValueCountFrequency (%)
727
 
0.2%
6327
 
2.4%
5.562
 
0.5%
51158
8.4%
4.5173
 
1.3%

47
Real number (ℝ≥0)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.337956895
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Memory size107.4 KiB
2021-04-07T21:36:53.846073image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median4
Q34
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.126626532
Coefficient of variation (CV)0.3375197964
Kurtosis-0.7032466475
Mean3.337956895
Median Absolute Deviation (MAD)1
Skewness-0.1727817967
Sum45843.5
Variance1.269287342
MonotocityNot monotonic
2021-04-07T21:36:53.980315image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
45248
38.2%
23316
24.1%
31687
 
12.3%
51109
 
8.1%
3.5653
 
4.8%
1542
 
3.9%
4.5492
 
3.6%
2.5350
 
2.5%
6222
 
1.6%
1.568
 
0.5%
Other values (2)47
 
0.3%
ValueCountFrequency (%)
1542
 
3.9%
1.568
 
0.5%
23316
24.1%
2.5350
 
2.5%
31687
12.3%
ValueCountFrequency (%)
73
 
< 0.1%
6222
 
1.6%
5.544
 
0.3%
51109
8.1%
4.5492
3.6%

48
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.70030581
Minimum0
Maximum4
Zeros2486
Zeros (%)18.1%
Memory size107.4 KiB
2021-04-07T21:36:54.158036image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile4
Maximum4
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.29230871
Coefficient of variation (CV)0.7600448706
Kurtosis-0.8192326812
Mean1.70030581
Median Absolute Deviation (MAD)1
Skewness0.4432925969
Sum23352
Variance1.670061801
MonotocityNot monotonic
2021-04-07T21:36:54.313977image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
23498
25.5%
13354
24.4%
02486
18.1%
42045
14.9%
31081
 
7.9%
0.5625
 
4.6%
1.5367
 
2.7%
2.5257
 
1.9%
3.521
 
0.2%
ValueCountFrequency (%)
02486
18.1%
0.5625
 
4.6%
13354
24.4%
1.5367
 
2.7%
23498
25.5%
ValueCountFrequency (%)
42045
14.9%
3.521
 
0.2%
31081
 
7.9%
2.5257
 
1.9%
23498
25.5%

49
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.552679482
Minimum0
Maximum4
Zeros2587
Zeros (%)18.8%
Memory size107.4 KiB
2021-04-07T21:36:54.479877image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum4
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.205256104
Coefficient of variation (CV)0.7762426945
Kurtosis-0.4818696987
Mean1.552679482
Median Absolute Deviation (MAD)1
Skewness0.602358391
Sum21324.5
Variance1.452642277
MonotocityNot monotonic
2021-04-07T21:36:54.627120image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
14544
33.1%
23052
22.2%
02587
18.8%
41422
 
10.4%
31316
 
9.6%
1.5430
 
3.1%
0.5287
 
2.1%
2.584
 
0.6%
3.512
 
0.1%
ValueCountFrequency (%)
02587
18.8%
0.5287
 
2.1%
14544
33.1%
1.5430
 
3.1%
23052
22.2%
ValueCountFrequency (%)
41422
10.4%
3.512
 
0.1%
31316
9.6%
2.584
 
0.6%
23052
22.2%

50
Real number (ℝ≥0)

ZEROS

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8536478812
Minimum0
Maximum22
Zeros5744
Zeros (%)41.8%
Memory size107.4 KiB
2021-04-07T21:36:54.790851image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum22
Range22
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9952129007
Coefficient of variation (CV)1.165835379
Kurtosis33.76597471
Mean0.8536478812
Median Absolute Deviation (MAD)1
Skewness2.822126719
Sum11724
Variance0.9904487178
MonotocityNot monotonic
2021-04-07T21:36:54.928735image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
05744
41.8%
14773
34.8%
21844
 
13.4%
3513
 
3.7%
0.5364
 
2.7%
1.5214
 
1.6%
4145
 
1.1%
566
 
0.5%
2.539
 
0.3%
612
 
0.1%
Other values (5)20
 
0.1%
ValueCountFrequency (%)
05744
41.8%
0.5364
 
2.7%
14773
34.8%
1.5214
 
1.6%
21844
 
13.4%
ValueCountFrequency (%)
222
 
< 0.1%
131
 
< 0.1%
84
 
< 0.1%
72
 
< 0.1%
612
0.1%

51
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7273554682
Minimum0
Maximum5
Zeros3687
Zeros (%)26.8%
Memory size107.4 KiB
2021-04-07T21:36:55.066828image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.5
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7266264922
Coefficient of variation (CV)0.9989977721
Kurtosis4.74228746
Mean0.7273554682
Median Absolute Deviation (MAD)0.5
Skewness1.835116309
Sum9989.5
Variance0.5279860592
MonotocityNot monotonic
2021-04-07T21:36:55.223584image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
14785
34.8%
0.54007
29.2%
03687
26.8%
2587
 
4.3%
3495
 
3.6%
475
 
0.5%
2.542
 
0.3%
1.540
 
0.3%
514
 
0.1%
3.52
 
< 0.1%
ValueCountFrequency (%)
03687
26.8%
0.54007
29.2%
14785
34.8%
1.540
 
0.3%
2587
 
4.3%
ValueCountFrequency (%)
514
 
0.1%
475
 
0.5%
3.52
 
< 0.1%
3495
3.6%
2.542
 
0.3%

52
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5349861657
Minimum0
Maximum5
Zeros6273
Zeros (%)45.7%
Memory size107.4 KiB
2021-04-07T21:36:55.396592image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.5
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7119528101
Coefficient of variation (CV)1.330787328
Kurtosis6.144803669
Mean0.5349861657
Median Absolute Deviation (MAD)0.5
Skewness2.1394815
Sum7347.5
Variance0.5068768039
MonotocityNot monotonic
2021-04-07T21:36:55.571928image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
06273
45.7%
0.53316
24.1%
13104
22.6%
2488
 
3.6%
3365
 
2.7%
1.584
 
0.6%
454
 
0.4%
2.531
 
0.2%
519
 
0.1%
ValueCountFrequency (%)
06273
45.7%
0.53316
24.1%
13104
22.6%
1.584
 
0.6%
2488
 
3.6%
ValueCountFrequency (%)
519
 
0.1%
454
 
0.4%
3365
2.7%
2.531
 
0.2%
2488
3.6%

53
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4480122324
Minimum0
Maximum5
Zeros6988
Zeros (%)50.9%
Memory size107.4 KiB
2021-04-07T21:36:55.763037image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6335587497
Coefficient of variation (CV)1.414155025
Kurtosis9.120620602
Mean0.4480122324
Median Absolute Deviation (MAD)0
Skewness2.493381181
Sum6153
Variance0.4013966893
MonotocityNot monotonic
2021-04-07T21:36:55.928237image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
06988
50.9%
13250
23.7%
0.53001
21.9%
3295
 
2.1%
1.578
 
0.6%
466
 
0.5%
241
 
0.3%
2.57
 
0.1%
56
 
< 0.1%
3.52
 
< 0.1%
ValueCountFrequency (%)
06988
50.9%
0.53001
21.9%
13250
23.7%
1.578
 
0.6%
241
 
0.3%
ValueCountFrequency (%)
56
 
< 0.1%
466
 
0.5%
3.52
 
< 0.1%
3295
2.1%
2.57
 
0.1%

54
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.505388088
Minimum0
Maximum4
Zeros6151
Zeros (%)44.8%
Memory size107.4 KiB
2021-04-07T21:36:56.098872image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.5
Q31
95-th percentile1
Maximum4
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6107189147
Coefficient of variation (CV)1.208415729
Kurtosis6.31366306
Mean0.505388088
Median Absolute Deviation (MAD)0.5
Skewness1.945738441
Sum6941
Variance0.3729775928
MonotocityNot monotonic
2021-04-07T21:36:56.249513image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
06151
44.8%
14061
29.6%
0.52983
21.7%
3188
 
1.4%
2167
 
1.2%
2.587
 
0.6%
451
 
0.4%
1.546
 
0.3%
ValueCountFrequency (%)
06151
44.8%
0.52983
21.7%
14061
29.6%
1.546
 
0.3%
2167
 
1.2%
ValueCountFrequency (%)
451
 
0.4%
3188
1.4%
2.587
0.6%
2167
1.2%
1.546
 
0.3%

55
Real number (ℝ≥0)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.70467453
Minimum0
Maximum4
Zeros3
Zeros (%)< 0.1%
Memory size107.4 KiB
2021-04-07T21:36:56.410551image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1.5
Q32
95-th percentile2.5
Maximum4
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6134882765
Coefficient of variation (CV)0.35988587
Kurtosis0.136191553
Mean1.70467453
Median Absolute Deviation (MAD)0.5
Skewness0.6116050166
Sum23412
Variance0.3763678654
MonotocityNot monotonic
2021-04-07T21:36:56.564633image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
14131
30.1%
23784
27.6%
1.53241
23.6%
2.51945
14.2%
3531
 
3.9%
499
 
0.7%
03
 
< 0.1%
ValueCountFrequency (%)
03
 
< 0.1%
14131
30.1%
1.53241
23.6%
23784
27.6%
2.51945
14.2%
ValueCountFrequency (%)
499
 
0.7%
3531
 
3.9%
2.51945
14.2%
23784
27.6%
1.53241
23.6%

56
Real number (ℝ≥0)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.661970293
Minimum0
Maximum5
Zeros15
Zeros (%)0.1%
Memory size107.4 KiB
2021-04-07T21:36:56.724379image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11.5
median1.5
Q32
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.5556123613
Coefficient of variation (CV)0.3343094421
Kurtosis3.899068659
Mean1.661970293
Median Absolute Deviation (MAD)0.5
Skewness1.245155477
Sum22825.5
Variance0.308705096
MonotocityNot monotonic
2021-04-07T21:36:56.870557image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1.54793
34.9%
24577
33.3%
13253
23.7%
3622
 
4.5%
2.5364
 
2.7%
473
 
0.5%
531
 
0.2%
015
 
0.1%
0.55
 
< 0.1%
3.51
 
< 0.1%
ValueCountFrequency (%)
015
 
0.1%
0.55
 
< 0.1%
13253
23.7%
1.54793
34.9%
24577
33.3%
ValueCountFrequency (%)
531
 
0.2%
473
 
0.5%
3.51
 
< 0.1%
3622
4.5%
2.5364
2.7%

57
Real number (ℝ≥0)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.718727246
Minimum0
Maximum5
Zeros4
Zeros (%)< 0.1%
Memory size107.4 KiB
2021-04-07T21:36:57.001892image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11.5
median1.5
Q32
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.5819841514
Coefficient of variation (CV)0.3386134436
Kurtosis5.063283005
Mean1.718727246
Median Absolute Deviation (MAD)0.5
Skewness1.374969515
Sum23605
Variance0.3387055525
MonotocityNot monotonic
2021-04-07T21:36:57.144551image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
25772
42.0%
1.53736
27.2%
13142
22.9%
3637
 
4.6%
2.5282
 
2.1%
568
 
0.5%
467
 
0.5%
3.526
 
0.2%
04
 
< 0.1%
ValueCountFrequency (%)
04
 
< 0.1%
13142
22.9%
1.53736
27.2%
25772
42.0%
2.5282
 
2.1%
ValueCountFrequency (%)
568
 
0.5%
467
 
0.5%
3.526
 
0.2%
3637
4.6%
2.5282
2.1%

58
Real number (ℝ≥0)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.628585991
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size107.4 KiB
2021-04-07T21:36:57.292588image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.5
Q32
95-th percentile2
Maximum5
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5816383315
Coefficient of variation (CV)0.3571431504
Kurtosis5.952649373
Mean1.628585991
Median Absolute Deviation (MAD)0.5
Skewness1.510113727
Sum22367
Variance0.3383031486
MonotocityNot monotonic
2021-04-07T21:36:57.441384image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
25646
41.1%
14403
32.1%
1.53032
22.1%
3384
 
2.8%
2.5116
 
0.8%
474
 
0.5%
573
 
0.5%
3.56
 
< 0.1%
ValueCountFrequency (%)
14403
32.1%
1.53032
22.1%
25646
41.1%
2.5116
 
0.8%
3384
 
2.8%
ValueCountFrequency (%)
573
 
0.5%
474
 
0.5%
3.56
 
< 0.1%
3384
2.8%
2.5116
 
0.8%

59
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.889799039
Minimum0
Maximum4
Zeros510
Zeros (%)3.7%
Memory size107.4 KiB
2021-04-07T21:36:57.599299image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12.5
median3
Q33.5
95-th percentile4
Maximum4
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9182907533
Coefficient of variation (CV)0.3177697622
Kurtosis1.888304765
Mean2.889799039
Median Absolute Deviation (MAD)0.5
Skewness-1.34135598
Sum39688.5
Variance0.8432579076
MonotocityNot monotonic
2021-04-07T21:36:57.740330image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
34586
33.4%
3.53138
22.8%
41954
14.2%
2.51416
 
10.3%
21297
 
9.4%
0510
 
3.7%
1458
 
3.3%
1.5352
 
2.6%
0.523
 
0.2%
ValueCountFrequency (%)
0510
 
3.7%
0.523
 
0.2%
1458
 
3.3%
1.5352
 
2.6%
21297
9.4%
ValueCountFrequency (%)
41954
14.2%
3.53138
22.8%
34586
33.4%
2.51416
 
10.3%
21297
 
9.4%

60
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
10566 
0.5
2145 
1.0
 
1023

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.5
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.010566
76.9%
0.52145
 
15.6%
1.01023
 
7.4%
2021-04-07T21:36:58.123749image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:36:58.242284image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.010566
76.9%
0.52145
 
15.6%
1.01023
 
7.4%

Most occurring characters

ValueCountFrequency (%)
024300
59.0%
.13734
33.3%
52145
 
5.2%
11023
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
024300
88.5%
52145
 
7.8%
11023
 
3.7%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
024300
59.0%
.13734
33.3%
52145
 
5.2%
11023
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
024300
59.0%
.13734
33.3%
52145
 
5.2%
11023
 
2.5%

61
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
1.0
6741 
0.5
2499 
1.5
2011 
2.0
1267 
0.0
1216 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row1.5
3rd row1.5
4th row1.0
5th row0.0
ValueCountFrequency (%)
1.06741
49.1%
0.52499
 
18.2%
1.52011
 
14.6%
2.01267
 
9.2%
0.01216
 
8.9%
2021-04-07T21:36:58.538253image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:36:58.644458image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.06741
49.1%
0.52499
 
18.2%
1.52011
 
14.6%
2.01267
 
9.2%
0.01216
 
8.9%

Most occurring characters

ValueCountFrequency (%)
.13734
33.3%
012939
31.4%
18752
21.2%
54510
 
10.9%
21267
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
012939
47.1%
18752
31.9%
54510
 
16.4%
21267
 
4.6%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
.13734
33.3%
012939
31.4%
18752
21.2%
54510
 
10.9%
21267
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
.13734
33.3%
012939
31.4%
18752
21.2%
54510
 
10.9%
21267
 
3.1%

62
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
1.0
8124 
1.5
1884 
0.0
1328 
0.5
1269 
2.0
1129 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row1.5
3rd row1.5
4th row0.5
5th row0.0
ValueCountFrequency (%)
1.08124
59.2%
1.51884
 
13.7%
0.01328
 
9.7%
0.51269
 
9.2%
2.01129
 
8.2%
2021-04-07T21:36:59.003000image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:36:59.126528image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.08124
59.2%
1.51884
 
13.7%
0.01328
 
9.7%
0.51269
 
9.2%
2.01129
 
8.2%

Most occurring characters

ValueCountFrequency (%)
.13734
33.3%
013178
32.0%
110008
24.3%
53153
 
7.7%
21129
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
013178
48.0%
110008
36.4%
53153
 
11.5%
21129
 
4.1%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
.13734
33.3%
013178
32.0%
110008
24.3%
53153
 
7.7%
21129
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
.13734
33.3%
013178
32.0%
110008
24.3%
53153
 
7.7%
21129
 
2.7%

63
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
6026 
1.0
5186 
0.5
2522 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.5
3rd row1.0
4th row0.5
5th row0.0
ValueCountFrequency (%)
0.06026
43.9%
1.05186
37.8%
0.52522
18.4%
2021-04-07T21:36:59.452839image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:36:59.557168image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.06026
43.9%
1.05186
37.8%
0.52522
18.4%

Most occurring characters

ValueCountFrequency (%)
019760
48.0%
.13734
33.3%
15186
 
12.6%
52522
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
019760
71.9%
15186
 
18.9%
52522
 
9.2%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
019760
48.0%
.13734
33.3%
15186
 
12.6%
52522
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
019760
48.0%
.13734
33.3%
15186
 
12.6%
52522
 
6.1%

64
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
5490 
1.0
5229 
0.5
3015 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0
ValueCountFrequency (%)
0.05490
40.0%
1.05229
38.1%
0.53015
22.0%
2021-04-07T21:36:59.871980image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:36:59.974473image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.05490
40.0%
1.05229
38.1%
0.53015
22.0%

Most occurring characters

ValueCountFrequency (%)
019224
46.7%
.13734
33.3%
15229
 
12.7%
53015
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
019224
70.0%
15229
 
19.0%
53015
 
11.0%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
019224
46.7%
.13734
33.3%
15229
 
12.7%
53015
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
019224
46.7%
.13734
33.3%
15229
 
12.7%
53015
 
7.3%

65
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.5
5107 
1.0
4938 
0.0
3689 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.5
3rd row0.5
4th row0.5
5th row1.0
ValueCountFrequency (%)
0.55107
37.2%
1.04938
36.0%
0.03689
26.9%
2021-04-07T21:37:00.275900image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:37:00.378907image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.55107
37.2%
1.04938
36.0%
0.03689
26.9%

Most occurring characters

ValueCountFrequency (%)
017423
42.3%
.13734
33.3%
55107
 
12.4%
14938
 
12.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
017423
63.4%
55107
 
18.6%
14938
 
18.0%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
017423
42.3%
.13734
33.3%
55107
 
12.4%
14938
 
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
017423
42.3%
.13734
33.3%
55107
 
12.4%
14938
 
12.0%

66
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
13299 
1.0
 
389
0.5
 
46

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.013299
96.8%
1.0389
 
2.8%
0.546
 
0.3%
2021-04-07T21:37:00.671623image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:37:00.774988image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.013299
96.8%
1.0389
 
2.8%
0.546
 
0.3%

Most occurring characters

ValueCountFrequency (%)
027033
65.6%
.13734
33.3%
1389
 
0.9%
546
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
027033
98.4%
1389
 
1.4%
546
 
0.2%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
027033
65.6%
.13734
33.3%
1389
 
0.9%
546
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
027033
65.6%
.13734
33.3%
1389
 
0.9%
546
 
0.1%

67
Real number (ℝ≥0)

ZEROS

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1655016747
Minimum0
Maximum9
Zeros12658
Zeros (%)92.2%
Memory size107.4 KiB
2021-04-07T21:37:00.886742image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.5
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9074565378
Coefficient of variation (CV)5.483065591
Kurtosis57.02053478
Mean0.1655016747
Median Absolute Deviation (MAD)0
Skewness7.320518414
Sum2273
Variance0.8234773679
MonotocityNot monotonic
2021-04-07T21:37:01.021623image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
012658
92.2%
0.5484
 
3.5%
1260
 
1.9%
894
 
0.7%
471
 
0.5%
246
 
0.3%
544
 
0.3%
2.527
 
0.2%
922
 
0.2%
619
 
0.1%
Other values (4)9
 
0.1%
ValueCountFrequency (%)
012658
92.2%
0.5484
 
3.5%
1260
 
1.9%
246
 
0.3%
2.527
 
0.2%
ValueCountFrequency (%)
922
 
0.2%
894
0.7%
73
 
< 0.1%
619
 
0.1%
544
0.3%

68
Real number (ℝ≥0)

ZEROS

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.630115043
Minimum0
Maximum12
Zeros11303
Zeros (%)82.3%
Memory size107.4 KiB
2021-04-07T21:37:01.164084image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.031535841
Coefficient of variation (CV)3.224071324
Kurtosis16.88239683
Mean0.630115043
Median Absolute Deviation (MAD)0
Skewness4.081381414
Sum8654
Variance4.127137874
MonotocityNot monotonic
2021-04-07T21:37:01.329954image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
011303
82.3%
3609
 
4.4%
0.5581
 
4.2%
1447
 
3.3%
11181
 
1.3%
10134
 
1.0%
5109
 
0.8%
299
 
0.7%
1274
 
0.5%
672
 
0.5%
Other values (11)125
 
0.9%
ValueCountFrequency (%)
011303
82.3%
0.5581
 
4.2%
1447
 
3.3%
299
 
0.7%
2.59
 
0.1%
ValueCountFrequency (%)
1274
0.5%
11181
1.3%
10.52
 
< 0.1%
10134
1.0%
9.53
 
< 0.1%

69
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01762050386
Minimum0
Maximum4
Zeros13591
Zeros (%)99.0%
Memory size107.4 KiB
2021-04-07T21:37:01.482091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4
Range4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1951005204
Coefficient of variation (CV)11.07235763
Kurtosis213.592416
Mean0.01762050386
Median Absolute Deviation (MAD)0
Skewness13.63416824
Sum242
Variance0.03806421307
MonotocityNot monotonic
2021-04-07T21:37:01.623239image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
013591
99.0%
170
 
0.5%
237
 
0.3%
314
 
0.1%
1.510
 
0.1%
410
 
0.1%
0.52
 
< 0.1%
ValueCountFrequency (%)
013591
99.0%
0.52
 
< 0.1%
170
 
0.5%
1.510
 
0.1%
237
 
0.3%
ValueCountFrequency (%)
410
 
0.1%
314
 
0.1%
237
0.3%
1.510
 
0.1%
170
0.5%

70
Real number (ℝ≥0)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.854994903
Minimum0
Maximum5
Zeros95
Zeros (%)0.7%
Memory size107.4 KiB
2021-04-07T21:37:01.772683image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11.5
median1.5
Q32
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.8026059079
Coefficient of variation (CV)0.4326728373
Kurtosis1.070100903
Mean1.854994903
Median Absolute Deviation (MAD)0.5
Skewness0.9817800468
Sum25476.5
Variance0.6441762433
MonotocityNot monotonic
2021-04-07T21:37:01.911914image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1.54484
32.6%
23072
22.4%
12888
21.0%
32134
15.5%
2.5454
 
3.3%
4262
 
1.9%
3.5166
 
1.2%
095
 
0.7%
594
 
0.7%
0.575
 
0.5%
ValueCountFrequency (%)
095
 
0.7%
0.575
 
0.5%
12888
21.0%
1.54484
32.6%
23072
22.4%
ValueCountFrequency (%)
594
 
0.7%
4.510
 
0.1%
4262
 
1.9%
3.5166
 
1.2%
32134
15.5%

71
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3157128295
Minimum0
Maximum4
Zeros9281
Zeros (%)67.6%
Memory size107.4 KiB
2021-04-07T21:37:02.047931image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.5
95-th percentile1.5
Maximum4
Range4
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.6235487292
Coefficient of variation (CV)1.975050334
Kurtosis11.55670462
Mean0.3157128295
Median Absolute Deviation (MAD)0
Skewness3.045737307
Sum4336
Variance0.3888130177
MonotocityNot monotonic
2021-04-07T21:37:02.182414image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
09281
67.6%
0.52302
 
16.8%
11384
 
10.1%
2304
 
2.2%
1.5196
 
1.4%
3163
 
1.2%
4100
 
0.7%
2.54
 
< 0.1%
ValueCountFrequency (%)
09281
67.6%
0.52302
 
16.8%
11384
 
10.1%
1.5196
 
1.4%
2304
 
2.2%
ValueCountFrequency (%)
4100
 
0.7%
3163
1.2%
2.54
 
< 0.1%
2304
2.2%
1.5196
1.4%

72
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
1.0
13734 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.013734
100.0%
2021-04-07T21:37:02.488447image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:37:02.584826image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.013734
100.0%

Most occurring characters

ValueCountFrequency (%)
113734
33.3%
.13734
33.3%
013734
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
113734
50.0%
013734
50.0%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
113734
33.3%
.13734
33.3%
013734
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
113734
33.3%
.13734
33.3%
013734
33.3%

73
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
1.0
13734 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.013734
100.0%
2021-04-07T21:37:02.825874image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:37:02.922182image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.013734
100.0%

Most occurring characters

ValueCountFrequency (%)
113734
33.3%
.13734
33.3%
013734
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
113734
50.0%
013734
50.0%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
113734
33.3%
.13734
33.3%
013734
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
113734
33.3%
.13734
33.3%
013734
33.3%

74
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
6330 
1.0
5533 
0.5
1871 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0
ValueCountFrequency (%)
0.06330
46.1%
1.05533
40.3%
0.51871
 
13.6%
2021-04-07T21:37:03.176047image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:37:03.276931image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.06330
46.1%
1.05533
40.3%
0.51871
 
13.6%

Most occurring characters

ValueCountFrequency (%)
020064
48.7%
.13734
33.3%
15533
 
13.4%
51871
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
020064
73.0%
15533
 
20.1%
51871
 
6.8%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
020064
48.7%
.13734
33.3%
15533
 
13.4%
51871
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
020064
48.7%
.13734
33.3%
15533
 
13.4%
51871
 
4.5%

75
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
7118 
1.0
5210 
0.5
1406 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.07118
51.8%
1.05210
37.9%
0.51406
 
10.2%
2021-04-07T21:37:03.548625image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:37:03.650758image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.07118
51.8%
1.05210
37.9%
0.51406
 
10.2%

Most occurring characters

ValueCountFrequency (%)
020852
50.6%
.13734
33.3%
15210
 
12.6%
51406
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
020852
75.9%
15210
 
19.0%
51406
 
5.1%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
020852
50.6%
.13734
33.3%
15210
 
12.6%
51406
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
020852
50.6%
.13734
33.3%
15210
 
12.6%
51406
 
3.4%

76
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
1.0
9365 
2.0
2482 
1.5
1199 
0.0
 
569
0.5
 
119

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.09365
68.2%
2.02482
 
18.1%
1.51199
 
8.7%
0.0569
 
4.1%
0.5119
 
0.9%
2021-04-07T21:37:03.920606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:37:04.024445image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.09365
68.2%
2.02482
 
18.1%
1.51199
 
8.7%
0.0569
 
4.1%
0.5119
 
0.9%

Most occurring characters

ValueCountFrequency (%)
.13734
33.3%
013104
31.8%
110564
25.6%
22482
 
6.0%
51318
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
013104
47.7%
110564
38.5%
22482
 
9.0%
51318
 
4.8%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
.13734
33.3%
013104
31.8%
110564
25.6%
22482
 
6.0%
51318
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
.13734
33.3%
013104
31.8%
110564
25.6%
22482
 
6.0%
51318
 
3.2%

77
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
2.0
5889 
0.0
3866 
1.0
3210 
1.5
661 
0.5
 
108

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.5
3rd row2.0
4th row1.5
5th row2.0
ValueCountFrequency (%)
2.05889
42.9%
0.03866
28.1%
1.03210
23.4%
1.5661
 
4.8%
0.5108
 
0.8%
2021-04-07T21:37:04.338649image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:37:04.444754image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
2.05889
42.9%
0.03866
28.1%
1.03210
23.4%
1.5661
 
4.8%
0.5108
 
0.8%

Most occurring characters

ValueCountFrequency (%)
016939
41.1%
.13734
33.3%
25889
 
14.3%
13871
 
9.4%
5769
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
016939
61.7%
25889
 
21.4%
13871
 
14.1%
5769
 
2.8%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
016939
41.1%
.13734
33.3%
25889
 
14.3%
13871
 
9.4%
5769
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
016939
41.1%
.13734
33.3%
25889
 
14.3%
13871
 
9.4%
5769
 
1.9%

78
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
2.0
4971 
1.0
4481 
0.0
3455 
1.5
523 
0.5
 
304

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row0.0
4th row2.0
5th row2.0
ValueCountFrequency (%)
2.04971
36.2%
1.04481
32.6%
0.03455
25.2%
1.5523
 
3.8%
0.5304
 
2.2%
2021-04-07T21:37:04.747321image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:37:04.852843image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
2.04971
36.2%
1.04481
32.6%
0.03455
25.2%
1.5523
 
3.8%
0.5304
 
2.2%

Most occurring characters

ValueCountFrequency (%)
016666
40.4%
.13734
33.3%
15004
 
12.1%
24971
 
12.1%
5827
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
016666
60.7%
15004
 
18.2%
24971
 
18.1%
5827
 
3.0%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
016666
40.4%
.13734
33.3%
15004
 
12.1%
24971
 
12.1%
5827
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
016666
40.4%
.13734
33.3%
15004
 
12.1%
24971
 
12.1%
5827
 
2.0%

79
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
7102 
1.0
6632 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0
ValueCountFrequency (%)
0.07102
51.7%
1.06632
48.3%
2021-04-07T21:37:05.136908image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:37:05.236820image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.07102
51.7%
1.06632
48.3%

Most occurring characters

ValueCountFrequency (%)
020836
50.6%
.13734
33.3%
16632
 
16.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
020836
75.9%
16632
 
24.1%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
020836
50.6%
.13734
33.3%
16632
 
16.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
020836
50.6%
.13734
33.3%
16632
 
16.1%

80
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
13532 
1.0
 
202

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.013532
98.5%
1.0202
 
1.5%
2021-04-07T21:37:05.505058image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T21:37:05.607074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.013532
98.5%
1.0202
 
1.5%

Most occurring characters

ValueCountFrequency (%)
027266
66.2%
.13734
33.3%
1202
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
027266
99.3%
1202
 
0.7%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
027266
66.2%
.13734
33.3%
1202
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
027266
66.2%
.13734
33.3%
1202
 
0.5%

Interactions

2021-04-07T21:33:28.920037image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-07T21:33:29.232526image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-07T21:33:29.392032image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-07T21:33:29.559944image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-07T21:33:29.722365image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-07T21:33:29.888273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-07T21:33:30.053784image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-07T21:33:30.219048image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-07T21:33:30.384110image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-07T21:33:30.549827image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-07T21:33:30.716782image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-07T21:33:30.891400image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-07T21:33:31.054204image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-07T21:33:31.217839image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-07T21:33:31.382997image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-07T21:33:31.556581image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-07T21:33:31.731432image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-07T21:33:31.887476image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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Correlations

2021-04-07T21:37:05.937452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-07T21:37:07.996950image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-07T21:37:10.036064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-07T21:37:12.062318image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-04-07T21:37:13.900264image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

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A simple visualization of nullity by column.
2021-04-07T21:36:33.407886image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

01234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768697071727374757677787980
03.030.059.294132181.602831107.01.01.01.01.01.02.00.01.03.02.00.01.02.03.01.01.00.01.01.02.00.01.03.02.00.01.02.03.01.01.01.01.01.04.00.00.00.011.00.00.00.03.02.02.02.00.01.01.01.01.01.01.02.02.04.00.00.50.50.01.00.00.00.00.00.01.00.01.01.01.00.01.00.02.00.00.0
19.026.041.837388158.77182585.01.01.01.00.51.53.00.52.02.02.50.51.02.02.01.01.01.00.51.03.00.51.52.02.00.51.01.51.51.02.01.01.01.03.50.50.00.09.00.00.00.02.53.00.52.01.00.01.50.00.01.51.52.01.03.50.51.51.50.51.00.50.00.00.00.01.50.01.01.01.01.01.00.52.00.01.0
23.024.058.774905174.93547881.01.01.00.50.50.52.50.51.02.53.00.01.02.02.01.01.50.52.00.52.50.51.02.53.00.01.02.01.50.51.51.00.51.03.51.00.00.08.00.00.00.03.05.01.01.01.00.50.00.00.51.51.51.51.52.50.01.51.51.00.00.50.00.00.00.02.00.01.01.00.00.01.02.00.01.00.0
34.522.049.812426160.22418688.51.01.00.51.00.53.00.00.52.51.51.00.02.51.51.01.50.51.00.52.50.00.52.02.01.51.02.52.00.51.50.51.01.03.50.50.00.09.50.00.00.03.01.01.02.00.03.00.50.01.02.02.01.01.52.00.01.00.50.50.00.50.00.00.00.01.50.51.01.00.00.01.01.52.01.00.0
48.031.062.270030191.703227132.01.01.00.00.00.01.00.02.02.03.00.00.03.01.00.01.00.00.00.02.00.02.02.02.00.01.02.01.00.01.01.00.50.04.00.00.00.09.01.00.00.03.05.02.02.01.00.00.00.00.02.01.01.01.03.00.00.00.00.01.01.00.00.00.00.03.00.51.01.01.00.01.02.02.00.00.0
510.030.078.936613169.722373106.01.01.01.01.01.01.00.01.01.03.00.01.03.02.01.01.01.01.01.02.00.01.03.02.00.01.02.03.01.02.01.01.00.53.50.50.00.011.00.00.00.04.05.03.03.03.01.03.00.00.01.03.02.01.02.00.00.00.00.00.00.00.00.00.00.01.00.01.01.00.01.01.00.01.00.00.0
60.036.058.774905174.93547881.01.01.00.50.50.52.50.51.02.53.00.01.02.02.01.01.50.52.00.52.50.51.02.53.00.01.02.01.50.51.51.00.51.03.00.00.00.013.00.00.00.02.04.01.03.02.00.50.00.00.51.51.51.51.52.50.01.51.51.00.00.50.00.00.00.02.50.01.01.00.01.02.02.00.01.00.0
74.526.084.299663186.32728498.01.01.01.02.51.52.50.01.02.02.51.50.51.51.51.01.51.02.51.52.00.01.02.02.50.51.01.52.01.01.00.00.00.04.01.00.50.014.00.00.00.03.02.02.00.00.00.00.00.01.02.01.01.01.04.00.51.02.01.01.00.00.00.00.00.01.00.01.01.00.01.02.02.02.00.00.0
84.030.070.657258176.027653108.01.01.01.01.01.03.01.00.03.02.01.01.02.03.01.02.01.03.02.03.01.00.03.02.01.01.02.03.01.02.01.01.01.04.00.00.00.010.00.00.00.02.04.02.03.00.03.00.00.00.03.01.01.01.04.00.01.01.00.50.00.00.00.05.00.01.00.01.01.01.00.01.02.02.01.00.0
90.030.070.529403187.778207113.01.01.00.51.50.53.00.52.03.01.51.00.52.02.01.01.00.51.50.52.50.51.52.01.51.51.02.02.01.01.50.00.01.03.00.00.00.013.00.00.00.01.02.04.04.00.01.00.00.00.01.01.01.02.03.50.00.00.01.01.00.00.00.00.00.03.01.01.01.00.00.01.00.00.00.00.0

Last rows

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137243.033.071.333359169.38905684.51.01.00.02.01.02.00.02.02.02.00.00.02.02.00.01.00.02.01.02.00.02.02.02.00.00.02.01.00.01.01.01.01.05.00.00.00.010.00.00.00.02.04.04.01.02.01.00.00.00.02.51.02.02.03.00.01.01.00.00.00.50.00.01.00.01.51.51.01.01.01.01.00.00.01.00.0
137251.032.071.333359169.389056101.01.01.01.01.01.03.01.02.01.01.01.01.02.03.01.01.01.01.01.02.01.02.03.01.02.01.01.02.01.01.01.01.01.04.00.00.00.010.00.00.00.04.05.02.01.01.02.00.00.00.02.51.01.01.01.00.00.00.01.00.00.01.07.011.00.03.02.01.01.01.00.01.00.00.01.00.0
137265.018.059.287609174.365170108.01.01.00.52.51.03.00.01.02.02.00.50.51.51.50.51.00.52.51.03.00.51.52.02.51.51.01.51.50.01.01.01.01.03.00.00.00.010.00.00.00.02.52.02.02.00.00.01.03.03.02.02.01.02.03.00.01.01.00.51.00.50.00.00.00.02.00.01.01.01.00.01.02.02.00.00.0
137270.022.058.774905174.93547881.01.01.00.50.50.52.50.51.02.53.00.01.02.02.01.01.50.52.00.52.50.51.02.53.00.01.02.01.50.51.51.00.50.04.00.00.00.012.00.00.00.03.04.00.00.00.00.50.00.01.01.51.51.51.02.50.01.51.51.00.00.50.00.00.00.02.00.01.01.00.01.01.02.01.01.00.0
137288.021.066.019276168.89899089.01.01.01.00.50.51.00.01.02.02.51.51.01.52.00.51.51.01.00.51.50.01.01.52.52.01.02.02.00.51.50.01.00.53.50.00.00.011.00.00.00.03.04.02.51.01.00.50.01.00.01.51.02.01.53.00.01.01.00.01.00.50.00.00.00.03.01.01.01.01.00.51.02.00.00.00.0
1372913.027.065.306425170.58311494.51.01.01.01.51.02.50.01.01.52.51.50.52.52.01.01.51.01.01.02.50.51.02.52.52.00.53.02.51.01.00.01.00.53.00.50.50.09.00.00.01.03.52.51.01.02.00.00.50.50.51.01.51.51.53.00.01.01.00.50.51.00.00.00.00.01.50.01.01.01.00.52.02.02.00.00.0
137306.029.066.753014166.41548588.01.01.00.01.01.02.01.01.02.03.01.01.03.03.01.01.00.01.01.02.01.01.02.02.01.01.03.02.00.01.01.01.01.05.00.01.00.09.00.00.00.01.01.04.04.02.01.00.51.01.02.51.51.01.03.00.01.01.01.00.00.00.00.53.00.02.00.51.01.00.50.02.00.00.01.00.0
1373121.037.058.075832169.235565108.01.01.01.02.02.02.00.00.01.02.01.01.03.03.01.02.01.02.02.02.00.00.01.02.01.01.03.03.01.02.01.01.01.04.01.00.00.06.01.00.00.02.02.04.04.00.04.00.00.50.03.01.02.01.04.00.01.02.00.01.00.00.00.00.00.01.50.51.01.01.00.00.00.00.01.00.0
1373210.030.068.706966170.274734103.01.01.01.02.00.02.01.00.02.02.01.01.01.03.01.01.00.02.00.01.01.00.01.01.02.01.00.02.00.01.01.01.01.05.01.01.01.08.01.00.01.03.04.03.02.01.00.50.51.00.02.02.02.02.04.00.00.00.01.00.00.50.00.00.00.01.00.01.01.00.00.01.00.02.01.00.0
137336.026.070.732913176.873882102.01.01.00.52.51.52.50.01.52.51.01.01.02.01.51.01.00.52.51.52.00.01.02.01.50.51.02.52.00.01.50.51.01.04.00.00.00.09.00.00.00.02.01.01.02.00.01.00.00.00.02.02.02.02.03.50.02.02.01.01.00.00.00.00.00.01.50.51.01.00.00.01.00.02.01.00.0

Duplicate rows

Most frequent

01234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768697071727374757677787980count
466.025.057.568818156.507062100.01.01.01.02.52.02.00.00.01.51.51.51.02.01.01.01.51.02.52.02.50.01.01.52.02.01.02.51.00.51.50.50.50.03.00.50.50.010.50.00.01.03.03.00.01.01.01.00.50.50.52.01.52.01.51.50.00.50.50.00.50.50.00.00.00.02.00.01.01.01.00.01.01.51.51.00.05
31.523.055.123227165.738286108.51.01.01.02.01.51.50.51.02.02.01.00.52.02.01.01.01.02.01.51.50.51.02.01.51.01.01.51.50.51.01.01.01.03.50.00.50.510.50.00.00.03.03.01.01.50.50.50.00.02.51.51.51.52.01.50.01.51.01.00.50.50.00.00.00.01.00.01.01.00.00.51.52.00.01.00.04
294.030.566.399717189.740717122.51.01.00.00.00.01.50.01.52.53.00.00.03.02.00.51.00.00.00.02.50.01.52.52.50.00.52.51.00.51.00.00.00.53.50.00.00.011.00.50.00.02.03.53.03.00.50.50.00.00.01.51.01.02.03.00.00.00.00.50.50.50.00.00.00.03.01.01.01.00.50.01.01.01.00.00.04
395.530.560.782081186.653029119.51.01.00.50.50.51.50.01.52.52.50.00.52.52.00.51.00.00.50.52.00.01.52.52.00.01.02.02.00.51.01.01.00.54.00.00.00.010.00.50.00.03.03.52.02.00.50.50.50.50.51.51.01.51.53.50.00.00.00.00.50.50.00.00.00.01.00.01.01.01.00.01.01.02.00.00.04
527.031.059.273429181.013750125.51.01.00.50.50.51.50.52.02.52.50.50.52.52.00.51.00.50.50.52.50.51.52.52.01.01.02.01.50.51.01.01.00.54.00.50.50.56.51.00.50.53.04.51.01.51.00.00.50.00.52.01.51.02.03.00.00.00.00.51.00.50.00.00.00.01.00.01.01.01.00.51.01.51.50.00.04
61.530.062.090271166.995661108.51.01.01.02.01.51.50.51.02.02.01.00.52.02.01.01.01.02.01.51.50.51.02.01.51.01.01.51.50.51.01.01.01.03.50.00.50.510.50.00.00.02.54.51.02.01.50.00.00.02.51.01.52.52.01.50.01.51.01.01.00.50.00.00.00.01.00.01.01.00.50.51.52.00.01.00.03
142.537.058.774905174.93547881.01.01.00.50.50.52.50.51.02.53.00.01.02.02.01.01.50.52.00.52.50.51.02.53.00.01.02.01.50.51.51.00.50.57.00.50.00.010.50.00.00.03.54.50.51.54.00.50.00.00.51.51.51.52.02.50.01.51.51.00.00.50.00.00.00.02.50.01.01.00.01.01.51.01.01.00.03
223.532.058.774905174.93547881.01.01.00.50.50.52.50.51.02.53.00.01.02.02.01.01.50.52.00.52.50.51.02.53.00.01.02.01.50.51.51.00.51.04.50.50.50.09.50.00.00.02.54.00.51.53.00.50.00.00.51.51.51.52.02.50.01.51.51.00.00.50.00.00.00.02.00.01.01.00.01.01.02.02.01.00.03
244.023.058.774905174.93547881.01.01.00.50.50.52.50.51.02.53.00.01.02.02.01.01.50.52.00.52.50.51.02.53.00.01.02.01.50.51.51.00.51.04.00.00.00.09.00.00.00.02.04.02.03.01.00.50.00.00.51.51.51.51.52.50.01.51.51.00.00.50.00.00.00.02.00.01.01.00.00.00.52.02.01.00.03
324.040.065.466287173.74380494.51.01.01.01.51.02.50.01.01.52.51.50.52.52.01.01.51.01.01.02.50.51.02.52.52.00.53.02.51.01.00.51.01.03.50.50.00.012.00.00.00.04.02.52.01.02.00.52.00.50.51.01.51.52.03.00.01.51.00.00.50.50.00.00.00.01.50.01.01.01.00.01.52.01.00.00.03